Personally, I don't know a way to access job configuration parameters in
custom implementation of Writables ( at least not an elegant and
appropriate one. Of course hacks of various kinds be done.) Maybe experts
can chime in?
One idea that I thought about was to use MapWritable (if you have not
explored it already.) You can encode the 'custom metadata' for you 'data'
as one byte symbols and move your data in the M/R flow as a map. Then while
deserialization you will have the type (or your 'custom metadata') in the
key part of the map and the value would be you actual data. This aligns
with the efficient approach that is used natively in Hadoop for
Strings/Text i.e. compact metadata (though I agree that you are not taking
advantage of the other aspect of non-dependence between metadata and the
data it defines.)
Take a look at that:
Page 96 of the Definitive Guide:
and then this:
and add your own custom types here (note that you are restricted by size of
On Sat, Aug 31, 2013 at 5:38 AM, Adrian CAPDEFIER <[EMAIL PROTECTED]>wrote:
> Thank you for your help Shahab.
> I guess I wasn't being too clear. My logic is that I use a custom type as
> key and in order to deserialize it on the compute nodes, I need an extra
> piece of information (also a custom type).
> To use an analogy, a Text is serialized by writing the length of the
> string as a number and then the bytes that compose the actual string. When
> it is deserialized, the number informs the reader when to stop reading the
> string. This number is varies from string to string and it is compact so it
> makes sense to serialize it with the string.
> My use case is similar to it. I have a complex type (let's call this
> data), and in order to deserialize it, I need another complex type (let's
> call this second type metadata). The metadata is not closely tied to the
> data (i.e. if the data value changes, the metadata does not) and the
> metadata size is quite large.
> I ruled out a couple of options, but please let me know if you think I did
> so for the wrong reasons:
> 1. I could serialize each data value with it's own metadata value, but
> since the data value count is in the +tens of millions and the metadata
> value distinct count can be up to one hundred, it would waste resources in
> the system.
> 2. I could serialize the metadata and then the data as a collection
> property of the metadata. This would be an elegant solution code-wise, but
> then all the data would have to be read and kept in memory as a massive
> object before any reduce operations can happen. I wasn't able to find any
> info on this online so this is just a guess from peeking at the hadoop code.
> My "solution" was to serialize the data with a hash of the metadata and
> separately serialize the metadata and its hash in the job configuration (as
> key/value pairs). For this to work, I would need to be able to deserialize
> the metadata on the reduce node before the data is deserialized in the
> readFields() method.
> I think that for that to happen I need to hook into the code somewhere
> where a context or job configuration is used (before readFields()), but I'm
> stumped as to where that is.
> On Sat, Aug 31, 2013 at 3:42 AM, Shahab Yunus <[EMAIL PROTECTED]>wrote:
>> What I meant was that you might have to split or redesign your logic or
>> your usecase (which we don't know about)?
>> On Fri, Aug 30, 2013 at 10:31 PM, Adrian CAPDEFIER <
>> [EMAIL PROTECTED]> wrote: